Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Falkiewicz, Maciej"'
Traditional molecule generation methods often rely on sequence or graph-based representations, which can limit their expressive power or require complex permutation-equivariant architectures. This paper introduces a novel paradigm for learning molecu
Externí odkaz:
http://arxiv.org/abs/2410.12522
We propose a novel deep generative model, the Kolmogorov-Smirnov Generative Adversarial Network (KSGAN). Unlike existing approaches, KSGAN formulates the learning process as a minimization of the Kolmogorov-Smirnov (KS) distance, generalized to handl
Externí odkaz:
http://arxiv.org/abs/2406.19948
Autor:
Falkiewicz, Maciej, Takeishi, Naoya, Shekhzadeh, Imahn, Wehenkel, Antoine, Delaunoy, Arnaud, Louppe, Gilles, Kalousis, Alexandros
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posi
Externí odkaz:
http://arxiv.org/abs/2310.13402
Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, wh
Externí odkaz:
http://arxiv.org/abs/1904.03423
Publikováno v:
In Knowledge-Based Systems 25 January 2022 236
